用于低剂量X射线CT的内部断层重建的深度学习方法
Changyu Chen1, Li Zhang2, Hewei Gao3
1Department of Engineering Physics, Tsinghua University, Room 602, Liuqing Building, Tsinghua University, Beijing, Beijing, 100084, CHINA.
Physics in medicine and biology
|September 3, 2025
概括
这项研究引入了两种深度学习方法,即DPER和DPER-Pro,用于低剂量内部断层扫描. 这些方法有效地重建高质量的感兴趣区域图像,同时扩大可回收区域,克服噪音和截断挑战.
科学领域:
- 医学成像
- 计算机成像
- 医学的人工智能
背景情况:
- 低剂量内部断层扫描将低剂量CT (LDCT) 与感兴趣区域 (ROI) 成像相结合,以减少剂量和高分辨率成像.
- 由于噪音和数据截断的综合影响,对准确的断层重建存在挑战.
- 开发新的重建框架对于高质量的ROI成像和可回收区域的有效扩展至关重要.
研究的目的:
- 开发一种基于深度学习的创新重建框架,用于低剂量内部断层扫描.
- 解决噪音和截断的投影造成的错误问题.
- 实现高质量的回报率重建和可回收区域的有效扩展.
主要方法:
- 在低剂量内部断层扫描中对投影数据组成和角取样模式进行了全面分析.
- 提出了两种基于深度学习的重建管道:基于深度投影的重建 (DPER) 和带有渐进扩展的DPER (DPER-Pro).
- DPER使用双域深度神经网络来解和提取 ROI 重建的噪音和背景投影贡献. 通过逐步的"粗到细"战略来补偿缺失的数据,DPER-Pro增强了DPER.
主要成果:
- 通过准确地提取噪声和背景预测,DPER有效地处理了与错误相结合的问题,实现了高质量的ROI重建.
- 通过利用分离的投影元件和角度采样模式,DPER-Pro扩展了可回收区域,同时保持了ROI图像质量.
- 这两种方法在重建可靠结构,增强泛化和减轻噪音和截断工件方面都优于竞争的方法.
结论:
- 拟议的脱深度学习框架为低剂量内部断层扫描提供了强有力的解决方案,有效地应对噪音和截断的投影所带来的挑战.
- 这些方法显著提高了ROI重建的质量,并有效地恢复了外部区域的结构信息.
- 这项工作为在各种应用中推进低剂量ROI成像提供了有前途的途径.
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